Advances in genomics and proteomics have created a demand for miniaturized, robust platforms for the high-throughput study of proteins. Microarrays, generated by spotting biomolecules on a solid surface at high spatial density, can offer these features by allowing investigators to query thousands of targets simultaneously. DNA microarrays including thousands of different DNA molecules or oligomeric sequences, for example, provide a snapshot of the transcriptional state of a biological sample. The widespread use of this technology for monitoring gene expression can provide valuable insight into various disease states. DNA microarrays can have particular value in analyzing clustered gene expression, revealing co-regulated gene networks; however, gene expression analysis does not readily predict protein abundance nor does it provide information about protein function.
Several properties of proteins make building protein microarrays more challenging than building their DNA counterparts. First, unlike the simple hybridization chemistry of nucleic acids, proteins demonstrate a staggering variety of chemistries, affinities and specificities. Moreover, proteins may require multimerization, partnership with other proteins or post-translational modification to demonstrate activity or binding. Second, there is no equivalent amplification process like PCR that can generate large quantities of protein. Third, expression and purification of proteins is a tedious task and does not guarantee the functional integrity of the protein. Lastly, many proteins are notoriously unstable, which raises concerns about microarray shelf life. Despite these challenges there has been a marked increase in the use of protein microarrays to map interactions of proteins with various other molecules, and to identify potential disease biomarkers, especially in the area of cancer biology.